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\citation{Fagerberg2010a}
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\citation{Overington2006a}
\citation{Claros1994}
\citation{Tusnady1998}
\citation{Krogh2001}
\citation{Kall2005}
\citation{Rost1996}
\citation{Yuan2004}
\citation{Nugent2009b}
\citation{Reynolds2008}
\citation{Rost2004a}
\citation{Consortium2011}
\citation{Tusnady2005}
\citation{Mika2003a}
\citation{Nugent2009b}
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\@writefile{toc}{\contentsline {section}{\numberline {1}Introduction}{1}{section.1}}
\@writefile{toc}{\contentsline {section}{\numberline {2}Methods}{1}{section.2}}
\@writefile{toc}{\contentsline {subsection}{\numberline {{2.1}}General approach}{1}{subsection.2.1}}
\@writefile{toc}{\contentsline {subsection}{\numberline {{2.2}}Dataset}{1}{subsection.2.2}}
\@writefile{toc}{\contentsline {subsection}{\numberline {{2.3}}Feature selection}{1}{subsection.2.3}}
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\citation{Hessa2007a}
\citation{Chawla2002}
\@writefile{lof}{\contentsline {figure}{\numberline {1}{\ignorespaces \textbf  {TMH prediction pipeline.} Features are mainly extracted from PredictProtein. Afterwards a protein is classified as either soluble or transmembrane by the SolTMH-predictor. Additionally transmembrane helices are assigned by the Helix-predictor. Both predictors employ a jury decision of three SVMs. }}{2}{figure.1}}
\newlabel{fig:pipeline}{{1}{2}{\textbf {TMH prediction pipeline.} Features are mainly extracted from PredictProtein. Afterwards a protein is classified as either soluble or transmembrane by the SolTMH-predictor. Additionally transmembrane helices are assigned by the Helix-predictor. Both predictors employ a jury decision of three SVMs. \relax }{figure.1}{}}
\@writefile{toc}{\contentsline {subsubsection}{\numberline {{{2.3}.1}}Additional features}{2}{subsubsection.2.3.1}}
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\citation{Chen2002}
\citation{Chen2002}
\citation{Hsu2010}
\@writefile{lot}{\contentsline {table}{\numberline {1}{\ignorespaces \textbf  {Allocation of sets in 3-fold cross-validation}. Shown is the rotation of the three independent sets during cross validation. If the SVM trained on the first set, then the parameters were optimized in the grid search using the second set and finally performance was assessed using the third set. This assignment holds true for both the SolTMH- as well as the Helix-predictor}}{3}{table.1}}
\newlabel{tbl:setmatching}{{1}{3}{\textbf {Allocation of sets in 3-fold cross-validation}. Shown is the rotation of the three independent sets during cross validation. If the SVM trained on the first set, then the parameters were optimized in the grid search using the second set and finally performance was assessed using the third set. This assignment holds true for both the SolTMH- as well as the Helix-predictor\label {tbl:setmatching}\relax }{table.1}{}}
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\newlabel{tbl:paramopt}{{3}{3}{\textbf {Optimisation results.} Optimized parameters for the SolTMH- and Helix-predictors. \textbf {C} and \textbf {Gamma} are given as $\log _2$.\label {tbl:paramopt}\relax }{table.3}{}}
\@writefile{toc}{\contentsline {subsection}{\numberline {{2.7}}Jury decision and output format}{3}{subsection.2.7}}
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\@writefile{lof}{\contentsline {figure}{\numberline {3}{\ignorespaces \textbf  {Results of second level parameter optimization.} Shown are the resulting Q\textsubscript  {ok} scores for the grid search performed on each of the three SVMs for the prediction of transmembrane helices. C and gamma are the cost and gamma parameters for the SVM and are given as $\qopname  \relax o{log}_2$. For each training set, the optimisation set is chosen as denoted in Table~\ref  {tbl:setmatching}. }}{4}{figure.3}}
\newlabel{fig:heat}{{3}{4}{\textbf {Results of second level parameter optimization.} Shown are the resulting Q\textsubscript {ok} scores for the grid search performed on each of the three SVMs for the prediction of transmembrane helices. C and gamma are the cost and gamma parameters for the SVM and are given as $\log _2$. For each training set, the optimisation set is chosen as denoted in Table~\ref {tbl:setmatching}. \relax }{figure.3}{}}
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\newlabel{tbl:level1results}{{4}{4}{\textbf {Performance evaluation of SolTMH-predictor.} \textbf {Set} denotes the training set of the SVM, the according optimization and final test set are chosen as outlined in Table~\ref {tbl:setmatching}.\label {tbl:level1results}\relax }{table.4}{}}
\bibdata{PP1.bib}
\bibcite{Chawla2002}{{1}{2002}{{Chawla {\em  et~al.}}}{{Chawla, Bowyer, Hall, and Kegelmeyer}}}
\@writefile{lot}{\contentsline {table}{\numberline {5}{\ignorespaces \textbf  {Performance evaluation of Helix-predictor.} \textbf  {Set} denotes the training set of the SVM, the according optimization and final test set are chosen as outlined in Table~\ref  {tbl:setmatching}. Definitions of the scores can be found in Table~\ref  {tbl:level2scores}. All scores are shown multiplied by 100.}}{5}{table.5}}
\newlabel{tbl:level2results}{{5}{5}{\textbf {Performance evaluation of Helix-predictor.} \textbf {Set} denotes the training set of the SVM, the according optimization and final test set are chosen as outlined in Table~\ref {tbl:setmatching}. Definitions of the scores can be found in Table~\ref {tbl:level2scores}. All scores are shown multiplied by 100.\label {tbl:level2results}\relax }{table.5}{}}
\@writefile{lof}{\contentsline {figure}{\numberline {4}{\ignorespaces \textbf  {Distribution of per protein Q2 scores.} Shown is a histogram of Q2 values calculated for every protein in all three sets of transmembrane proteins.}}{5}{figure.4}}
\newlabel{fig:q2prot}{{4}{5}{\textbf {Distribution of per protein Q2 scores.} Shown is a histogram of Q2 values calculated for every protein in all three sets of transmembrane proteins}{figure.4}{}}
\@writefile{toc}{\contentsline {section}{\numberline {4}Conclusion and outlook}{5}{section.4}}
\@writefile{lof}{\contentsline {figure}{\numberline {5}{\ignorespaces \textbf  {Helix length distribution.} Displayed is the helix length distribution for all observed helices in the testing sets (green) and also all predicted helices (red).}}{5}{figure.5}}
\newlabel{fig:helixlengths}{{5}{5}{\textbf {Helix length distribution.} Displayed is the helix length distribution for all observed helices in the testing sets (green) and also all predicted helices (red)}{figure.5}{}}
\bibcite{Chen2002}{{2}{2002}{{Chen {\em  et~al.}}}{{Chen, Kernytsky, and Rost}}}
\bibcite{Claros1994}{{3}{1994}{{Claros and {Von Heijne}}}{{Claros and {Von Heijne}}}}
\bibcite{Fagerberg2010a}{{4}{2010}{{Fagerberg {\em  et~al.}}}{{Fagerberg, Jonasson, and von Heijne}}}
\bibcite{Hessa2007a}{{5}{2007}{{Hessa {\em  et~al.}}}{{Hessa, Meindl-Beinker, Bernsel, Kim, Sato, Lerch-Bader, Nilsson, White, and von Heijne}}}
\bibcite{Hsu2010}{{6}{2010}{{Hsu {\em  et~al.}}}{{Hsu, Chang, and Lin}}}
\bibcite{Jacoby2006a}{{7}{2006}{{Jacoby {\em  et~al.}}}{{Jacoby, Bouhelal, Gerspacher, and Seuwen}}}
\bibcite{Kall2005}{{8}{2005}{{K\"{a}ll {\em  et~al.}}}{{K\"{a}ll, Krogh, and Sonnhammer}}}
\bibcite{Krogh2001}{{9}{2001}{{Krogh {\em  et~al.}}}{{Krogh, Larsson, von Heijne, and Sonnhammer}}}
\bibcite{Mika2003a}{{10}{2003}{{Mika and Rost}}{{Mika and Rost}}}
\bibcite{Nugent2009b}{{11}{2009}{{Nugent and Jones}}{{Nugent and Jones}}}
\bibcite{Overington2006a}{{12}{2006}{{Overington {\em  et~al.}}}{{Overington, Al-Lazikani, and Hopkins}}}
\bibcite{Reynolds2008}{{13}{2008}{{Reynolds {\em  et~al.}}}{{Reynolds, K\"{a}ll, Riffle, Bilmes, and Noble}}}
\bibcite{Rost1996}{{14}{1996}{{Rost {\em  et~al.}}}{{Rost, Fariselli, and Casadio}}}
\bibcite{Rost2004a}{{15}{2004}{{Rost {\em  et~al.}}}{{Rost, Yachdav, and Liu}}}
\bibcite{Stevens2000a}{{16}{2000}{{Stevens and Arkin}}{{Stevens and Arkin}}}
\bibcite{Consortium2011}{{17}{2011}{{The Uniprot Consortium}}{{The Uniprot Consortium}}}
\bibcite{Tusnady1998}{{18}{1998}{{Tusn\'{a}dy and Simon}}{{Tusn\'{a}dy and Simon}}}
\bibcite{Tusnady2005}{{19}{2005}{{Tusn\'{a}dy {\em  et~al.}}}{{Tusn\'{a}dy, Doszt\'{a}nyi, and Simon}}}
\bibcite{Yuan2004}{{20}{2004}{{Yuan {\em  et~al.}}}{{Yuan, Mattick, and Teasdale}}}
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